AI-Powered Safety Monitoring for Your Construction Site

General contractors often use project management software like Procore or Autodesk Construction Cloud for incident reporting. These are excellent systems of record, but they are not real-time monitoring tools. Site safety depends on a manager physically observing the site or manually reviewing hours of security camera footage, often long after an incident occurs.

For example, consider a 25-person contracting firm with 8 cameras on site. A subcontractor's employee walks under a suspended load. No one notices at the moment. The safety manager only sees the event two days later while reviewing footage for an unrelated issue. The near-miss was never documented at the time, the behavior was not corrected, and the site risk remains unaddressed.

Some video management systems (VMS) offer basic "AI" features like person detection, but they lack construction-specific intelligence. These systems cannot distinguish a worker wearing a hard hat from one without, nor can they identify when a worker enters a dangerous exclusion zone around heavy machinery. The alerts are too generic to be useful for enforcing OSHA compliance.

The structural problem is that project management tools are designed for documentation, and security camera platforms are designed for intrusion detection. Neither is architected for the high-stakes, nuanced, real-time analysis required for proactive safety monitoring on a dynamic construction site. They lack the domain-specific models to turn raw video into actionable safety intelligence.

The first step would be an audit of your existing site cameras and safety protocols. Syntora would map camera locations, viewing angles, and resolutions to determine coverage. We would then document your specific safety checklist: what PPE is required in which zones, what are the high-risk activities, and how are incidents currently reported? This discovery produces a clear specification for what the AI needs to detect.

The core of the system would be a computer vision model running on AWS Lambda, processing frames from your camera streams. We would use a model like YOLOv8 and fine-tune it on images of your specific site conditions to recognize hard hats, vests, and other PPE with high accuracy. When a violation is detected, a Python script sends an alert with a timestamped image to a safety manager. Claude API could then be used to generate a structured, text-based incident summary from the image metadata for easy logging.

The delivered system connects to your existing camera feeds, typically via RTSP streams, without requiring new hardware. You receive a simple dashboard, built on Vercel, showing a log of all detected events with images and timestamps. All alerts are also stored in a Supabase database for trend analysis. You receive the full source code and a runbook explaining how to maintain and update the system.